An important factor behind the increasing use of ML in fields such as electron microscopy has been the development and access to open-source software. For example, software libraries such as tensorflowhub (https://www.tensorflow.org/hub) and segmentation_models [1]}, that provide access to pre-trained models, have aided in the application of ML models in fields where large curated training datasets are hard to find. In such cases, it becomes necessary to reuse models that have been pre-trained on large datasets such as the Imagenet [2]}. This is the case with electron microscopy, where generating good quality image data is expensive, time-consuming, and requires a high degree of technical skill. In addition, the creation of manually annotated ground truth data requires additional time from human experts. Thus, reusing pre-trained models can help in making the implementation of ML models more efficient. Examples of transfer learning in the field of ML for microscopy analysis include [3]} and [4]}.
